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import torch
import torch.nn.functional as F
import cv2
import numpy as np
import os
from glob import glob
from icecream import ic
from scipy.spatial.transform import Rotation as Rot
from scipy.spatial.transform import Slerp
import PIL.Image
from glob import glob
import pdb
def camNormal2worldNormal(rot_c2w, camNormal):
H,W,_ = camNormal.shape
normal_img = np.matmul(rot_c2w[None, :, :], camNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3])
return normal_img
def worldNormal2camNormal(rot_w2c, worldNormal):
H,W,_ = worldNormal.shape
normal_img = np.matmul(rot_w2c[None, :, :], worldNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3])
return normal_img
def trans_normal(normal, RT_w2c, RT_w2c_target):
normal_world = camNormal2worldNormal(np.linalg.inv(RT_w2c[:3,:3]), normal)
normal_target_cam = worldNormal2camNormal(RT_w2c_target[:3,:3], normal_world)
return normal_target_cam
def img2normal(img):
return (img/255.)*2-1
def normal2img(normal):
return np.uint8((normal*0.5+0.5)*255)
def norm_normalize(normal, dim=-1):
normal = normal/(np.linalg.norm(normal, axis=dim, keepdims=True)+1e-6)
return normal
def RT_opengl2opencv(RT):
# Build the coordinate transform matrix from world to computer vision camera
# R_world2cv = R_bcam2cv@R_world2bcam
# T_world2cv = R_bcam2cv@T_world2bcam
R = RT[:3, :3]
t = RT[:3, 3]
R_bcam2cv = np.asarray([[1, 0, 0], [0, -1, 0], [0, 0, -1]], np.float32)
R_world2cv = R_bcam2cv @ R
t_world2cv = R_bcam2cv @ t
RT = np.concatenate([R_world2cv,t_world2cv[:,None]],1)
return RT
def normal_opengl2opencv(normal):
H,W,C = np.shape(normal)
# normal_img = np.reshape(normal, (H*W,C))
R_bcam2cv = np.array([1, -1, -1], np.float32)
normal_cv = normal * R_bcam2cv[None, None, :]
print(np.shape(normal_cv))
return normal_cv
def inv_RT(RT):
RT_h = np.concatenate([RT, np.array([[0,0,0,1]])], axis=0)
RT_inv = np.linalg.inv(RT_h)
return RT_inv[:3, :]
def load_a_prediction(root_dir, test_object, imSize, view_types, load_color=False, cam_pose_dir=None, normal_system='front'):
all_images = []
all_normals = []
all_normals_world = []
all_masks = []
all_poses = []
all_w2cs = []
print(cam_pose_dir)
RT_front = np.loadtxt(glob(os.path.join(cam_pose_dir, '*_%s_RT.txt'%( 'front')))[0]) # world2cam matrix
RT_front_cv = RT_opengl2opencv(RT_front) # convert normal from opengl to opencv
for idx, view in enumerate(view_types):
print(os.path.join(root_dir,test_object))
normal_filepath = os.path.join(root_dir,test_object, 'normals_000_%s.png'%( view))
# Load key frame
if load_color: # use bgr
image =np.array(PIL.Image.open(normal_filepath.replace("normals", "rgb")).resize(imSize))[:, :, ::-1]
normal = np.array(PIL.Image.open(normal_filepath).resize(imSize))
mask = normal[:, :, 3]
normal = normal[:, :, :3]
RT = np.loadtxt(os.path.join(cam_pose_dir, '000_%s_RT.txt'%( view))) # world2cam matrix
normal = img2normal(normal)
normal[mask==0] = [0,0,0]
mask = mask> (0.5*255)
if load_color:
all_images.append(image)
all_masks.append(mask)
RT_cv = RT_opengl2opencv(RT) # convert normal from opengl to opencv
all_poses.append(inv_RT(RT_cv)) # cam2world
all_w2cs.append(RT_cv)
# whether to
normal_cam_cv = normal_opengl2opencv(normal)
if normal_system == 'front':
normal_world = camNormal2worldNormal(inv_RT(RT_front_cv)[:3, :3], normal_cam_cv)
elif normal_system == 'self':
normal_world = camNormal2worldNormal(inv_RT(RT_cv)[:3, :3], normal_cam_cv)
all_normals.append(normal_cam_cv)
all_normals_world.append(normal_world)
if not load_color:
all_images = [normal2img(x) for x in all_normals_world]
return np.stack(all_images), np.stack(all_masks), np.stack(all_normals), np.stack(all_normals_world), np.stack(all_poses), np.stack(all_w2cs)
class Dataset:
def __init__(self, conf):
super(Dataset, self).__init__()
print('Load data: Begin')
self.device = torch.device('cuda')
self.conf = conf
self.data_dir = conf.get_string('data_dir')
self.object_name = conf.get_string('object_name')
self.object_viewidx = conf.get_int('object_viewidx')
self.imSize = conf['imSize']
self.load_color = conf['load_color']
self.stage = conf['stage']
self.mtype = conf['mtype']
self.num_views = conf['num_views']
self.normal_system = conf['normal_system']
self.cam_pose_dir = "./models/fixed_poses/"
if self.num_views == 4:
view_types = ['front', 'right', 'back', 'left']
elif self.num_views == 5:
view_types = ['front', 'front_right', 'right', 'back', 'left']
elif self.num_views == 6:
view_types = ['front', 'front_right', 'right', 'back', 'left', 'front_left']
self.images_np, self.masks_np, self.normals_cam_np, \
self.normals_world_np ,self.pose_all_np, self.w2c_all_np = load_a_prediction(
self.data_dir, self.object_name, self.imSize, view_types, self.load_color,
self.cam_pose_dir, normal_system=self.normal_system)
self.n_images = self.images_np.shape[0]
self.images = torch.from_numpy(self.images_np.astype(np.float32)).cpu() / 255. # [n_images, H, W, 3]
self.masks = torch.from_numpy(self.masks_np.astype(np.float32)).cpu() # [n_images, H, W, 3]
self.normals_cam = torch.from_numpy(self.normals_cam_np.astype(np.float32)).cpu() # [n_images, H, W, 3]
self.normals_world = torch.from_numpy(self.normals_world_np.astype(np.float32)).cpu() # [n_images, H, W, 3]
self.pose_all = torch.from_numpy(self.pose_all_np.astype(np.float32)).cpu() # [n_images,3, 4] cam2world
# self.pose_all = torch.stack(self.pose_all).to(self.device) # [n_images, 4, 4]
self.H, self.W = self.images.shape[1], self.images.shape[2]
self.image_pixels = self.H * self.W
self.intrinsic = torch.from_numpy(np.array([
[self.W/2.0, 0, self.W / 2.0, 0],
[0, self.H/2.0, self.H/ 2.0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
]).astype(np.float32))
self.intrinsics_all = torch.stack([self.intrinsic]*self.num_views, dim=0).cpu()
self.intrinsics_all_inv = torch.inverse(self.intrinsics_all).cpu() # [n_images, 4, 4]
object_bbox_min = np.array([-1.01, -1.01, -1.01, 1.0])
object_bbox_max = np.array([ 1.01, 1.01, 1.01, 1.0])
self.object_bbox_min = object_bbox_min[:3]
self.object_bbox_max = object_bbox_max[:3]
self.near = 0.2
self.far = 2.4
self.cos = torch.nn.CosineSimilarity(dim=1, eps=1e-6)
self.all_rays = self.prepare_all_rays()
print('Load data: End')
def gen_rays_at(self, img_idx, resolution_level=1):
"""
Generate rays at world space from one camera.
"""
l = resolution_level
tx = torch.linspace(0, self.W - 1, self.W // l)
ty = torch.linspace(0, self.H - 1, self.H // l)
pixels_x, pixels_y = torch.meshgrid(tx, ty)
q = torch.stack([(pixels_x/self.W-0.5)*2, (pixels_y/self.H-0.5)*2, torch.zeros_like(pixels_y)], dim=-1) # W, H, 3
v = torch.stack([torch.zeros_like(pixels_y), torch.zeros_like(pixels_y), torch.ones_like(pixels_y)], dim=-1) # W, H, 3
# orthogonal projection
rays_v = v / torch.linalg.norm(v, ord=2, dim=-1, keepdim=True) # W, H, 3
rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3].cuda(), rays_v[:, :, :, None].cuda()).squeeze() # W, H, 3
rays_o = torch.matmul(self.pose_all[img_idx, None, None, :3, :3].cuda(), q[:, :, :, None].cuda()).squeeze() # W, H, 3
rays_o = self.pose_all[img_idx, None, None, :3, 3].expand(rays_v.shape).cuda() + rays_o # W, H, 3
return rays_o.transpose(0, 1), rays_v.transpose(0, 1)
def gen_random_rays_at(self, img_idx, batch_size):
"""
Generate random rays at world space from one camera.
"""
pixels_x = torch.randint(low=0, high=self.W, size=[batch_size]).cpu()
pixels_y = torch.randint(low=0, high=self.H, size=[batch_size]).cpu()
color = self.images[img_idx][(pixels_y, pixels_x)] # batch_size, 3
mask = self.masks[img_idx][(pixels_y, pixels_x)] # batch_size, 3
normal = self.normals_world[img_idx][(pixels_y, pixels_x)] # batch_size, 3
q = torch.stack([(pixels_x / self.W-0.5)*2, (pixels_y / self.H-0.5)*2, torch.zeros_like(pixels_y)], dim=-1).float() # batch_size, 3
v = torch.stack([torch.zeros_like(pixels_y), torch.zeros_like(pixels_y), torch.ones_like(pixels_y)], dim=-1).float()
# q = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1).float() # bsz, 3
# q = torch.matmul(self.intrinsics_all_inv[img_idx, None, :3, :3], q[:, :, None]).squeeze() # bsz, 3
# q[:, 2] = 0
rays_v = v / torch.linalg.norm(v, ord=2, dim=-1, keepdim=True) # batch_size, 3
rays_v = torch.matmul(self.pose_all[img_idx, None, :3, :3], rays_v[:, :, None]).squeeze() # batch_size, 3
rays_o = torch.matmul(self.pose_all[img_idx, None, :3, :3], q[:, :, None]).squeeze() # batch_size, 3
rays_o = self.pose_all[img_idx, None, :3, 3].expand(rays_v.shape) + rays_o # batch_size, 3
return torch.cat([rays_o.cpu(), rays_v.cpu(), color, mask[:, None], normal], dim=-1).cuda() # batch_size, 10
def prepare_rays_a_view(self, img_idx):
"""
Generate random rays at world space from one camera.
"""
tx = torch.linspace(0, self.W - 1, self.W)
ty = torch.linspace(0, self.H - 1, self.H)
pixels_x, pixels_y = torch.meshgrid(tx, ty)
pixels_x = pixels_x.reshape(-1).long()
pixels_y = pixels_y.reshape(-1).long()
color = self.images[img_idx][(pixels_y, pixels_x)] # batch_size, 3
mask = self.masks[img_idx][(pixels_y, pixels_x)] # batch_size, 3
normal = self.normals_world[img_idx][(pixels_y, pixels_x)] # batch_size, 3
q = torch.stack([(pixels_x / self.W-0.5)*2, (pixels_y / self.H-0.5)*2, torch.zeros_like(pixels_y)], dim=-1).float() # batch_size, 3
v = torch.stack([torch.zeros_like(pixels_y), torch.zeros_like(pixels_y), torch.ones_like(pixels_y)], dim=-1).float()
rays_v = v / torch.linalg.norm(v, ord=2, dim=-1, keepdim=True) # batch_size, 3
rays_v = torch.matmul(self.pose_all[img_idx, None, :3, :3], rays_v[:, :, None]).squeeze() # batch_size, 3
rays_o = torch.matmul(self.pose_all[img_idx, None, :3, :3], q[:, :, None]).squeeze() # batch_size, 3
rays_o = self.pose_all[img_idx, None, :3, 3].expand(rays_v.shape) + rays_o # batch_size, 3
cosines = self.cos(rays_v, normal)
# pdb.set_trace()
return torch.cat([rays_o.cpu(), rays_v.cpu(), color, mask[:, None], normal, cosines[:, None]], dim=-1) # batch_size, 10
def prepare_all_rays(self,):
all_rays = []
for idx in range(self.n_images):
rays = self.prepare_rays_a_view(idx)
all_rays.append(rays)
all_rays = torch.concat(all_rays, dim=0)
return all_rays
def __getitem__(self, idx):
return self.all_rays[idx]
def __len__(self):
return self.all_rays.shape[0]
def gen_rays_between(self, idx_0, idx_1, ratio, resolution_level=1):
"""
Interpolate pose between two cameras.
"""
l = resolution_level
tx = torch.linspace(0, self.W - 1, self.W // l)
ty = torch.linspace(0, self.H - 1, self.H // l)
pixels_x, pixels_y = torch.meshgrid(tx, ty)
p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3
p = torch.matmul(self.intrinsics_all_inv[0, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3
rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # W, H, 3
trans = self.pose_all[idx_0, :3, 3] * (1.0 - ratio) + self.pose_all[idx_1, :3, 3] * ratio
pose_0 = self.pose_all[idx_0].detach().cpu().numpy()
pose_1 = self.pose_all[idx_1].detach().cpu().numpy()
pose_0 = np.linalg.inv(pose_0)
pose_1 = np.linalg.inv(pose_1)
rot_0 = pose_0[:3, :3]
rot_1 = pose_1[:3, :3]
rots = Rot.from_matrix(np.stack([rot_0, rot_1]))
key_times = [0, 1]
slerp = Slerp(key_times, rots)
rot = slerp(ratio)
pose = np.diag([1.0, 1.0, 1.0, 1.0])
pose = pose.astype(np.float32)
pose[:3, :3] = rot.as_matrix()
pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3]
pose = np.linalg.inv(pose)
rot = torch.from_numpy(pose[:3, :3]).cuda()
trans = torch.from_numpy(pose[:3, 3]).cuda()
rays_v = torch.matmul(rot[None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3
rays_o = trans[None, None, :3].expand(rays_v.shape) # W, H, 3
return rays_o.transpose(0, 1), rays_v.transpose(0, 1)
def near_far_from_sphere(self, rays_o, rays_d):
a = torch.sum(rays_d**2, dim=-1, keepdim=True)
b = 2.0 * torch.sum(rays_o * rays_d, dim=-1, keepdim=True)
mid = 0.5 * (-b) / a
near = mid - 1.0
far = mid + 1.0
return near, far
def get_near_far(self,):
return self.near, self.far
def image_at(self, idx, resolution_level):
img = self.images_np[idx]
return (cv2.resize(img, (self.W // resolution_level, self.H // resolution_level))).clip(0, 255)
def normal_cam_at(self, idx, resolution_level):
normal_cam = self.normals_cam_np[idx]
img = normal2img(normal_cam)
return (cv2.resize(img, (self.W // resolution_level, self.H // resolution_level))).clip(0, 255)
def mask_at(self, idx, resolution_level):
mask = np.uint8(self.masks_np[idx]*255)[:, :, None]
mask = np.concatenate([mask]*3, axis=-1)
return (cv2.resize(mask, (self.W // resolution_level, self.H // resolution_level))).clip(0, 255)
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